Overview

Dataset statistics

Number of variables13
Number of observations1038
Missing cells7
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory105.6 KiB
Average record size in memory104.1 B

Variable types

Text1
Numeric12

Alerts

Release date is highly overall correlated with Max resolution and 2 other fieldsHigh correlation
Max resolution is highly overall correlated with Release date and 2 other fieldsHigh correlation
Low resolution is highly overall correlated with Release date and 2 other fieldsHigh correlation
Effective pixels is highly overall correlated with Release date and 2 other fieldsHigh correlation
Weight (inc. batteries) is highly overall correlated with DimensionsHigh correlation
Dimensions is highly overall correlated with Weight (inc. batteries)High correlation
Model has unique valuesUnique
Low resolution has 54 (5.2%) zerosZeros
Effective pixels has 35 (3.4%) zerosZeros
Zoom wide (W) has 85 (8.2%) zerosZeros
Zoom tele (T) has 85 (8.2%) zerosZeros
Normal focus range has 137 (13.2%) zerosZeros
Macro focus range has 127 (12.2%) zerosZeros
Storage included has 123 (11.8%) zerosZeros
Weight (inc. batteries) has 21 (2.0%) zerosZeros
Dimensions has 14 (1.3%) zerosZeros

Reproduction

Analysis started2023-09-19 02:12:28.310979
Analysis finished2023-09-19 02:12:52.570879
Duration24.26 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Model
Text

UNIQUE 

Distinct1038
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size8.2 KiB
2023-09-18T22:12:53.102103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length25
Mean length16.782274
Min length8

Characters and Unicode

Total characters17420
Distinct characters65
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1038 ?
Unique (%)100.0%

Sample

1st rowAgfa ePhoto 1280
2nd rowAgfa ePhoto 1680
3rd rowAgfa ePhoto CL18
4th rowAgfa ePhoto CL30
5th rowAgfa ePhoto CL30 Clik!
ValueCountFrequency (%)
olympus 122
 
4.3%
sony 116
 
4.1%
canon 115
 
4.0%
kodak 102
 
3.6%
fujifilm 99
 
3.5%
powershot 97
 
3.4%
nikon 90
 
3.2%
finepix 88
 
3.1%
zoom 80
 
2.8%
coolpix 73
 
2.6%
Other values (1025) 1869
65.6%
2023-09-18T22:12:54.040270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1825
 
10.5%
o 1344
 
7.7%
i 1054
 
6.1%
0 933
 
5.4%
n 800
 
4.6%
a 741
 
4.3%
S 636
 
3.7%
C 571
 
3.3%
m 538
 
3.1%
P 514
 
3.0%
Other values (55) 8464
48.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8571
49.2%
Uppercase Letter 4059
23.3%
Decimal Number 2554
 
14.7%
Space Separator 1825
 
10.5%
Dash Punctuation 399
 
2.3%
Other Punctuation 12
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1344
15.7%
i 1054
12.3%
n 800
 
9.3%
a 741
 
8.6%
m 538
 
6.3%
s 413
 
4.8%
l 389
 
4.5%
t 373
 
4.4%
u 362
 
4.2%
x 357
 
4.2%
Other values (15) 2200
25.7%
Uppercase Letter
ValueCountFrequency (%)
S 636
15.7%
C 571
14.1%
P 514
12.7%
D 394
9.7%
F 296
 
7.3%
O 198
 
4.9%
Z 164
 
4.0%
E 154
 
3.8%
L 150
 
3.7%
M 132
 
3.3%
Other values (14) 850
20.9%
Decimal Number
ValueCountFrequency (%)
0 933
36.5%
5 284
 
11.1%
3 275
 
10.8%
1 254
 
9.9%
2 198
 
7.8%
7 184
 
7.2%
4 162
 
6.3%
6 108
 
4.2%
8 100
 
3.9%
9 56
 
2.2%
Other Punctuation
ValueCountFrequency (%)
/ 5
41.7%
* 5
41.7%
! 1
 
8.3%
. 1
 
8.3%
Space Separator
ValueCountFrequency (%)
1825
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 399
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12630
72.5%
Common 4790
 
27.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 1344
 
10.6%
i 1054
 
8.3%
n 800
 
6.3%
a 741
 
5.9%
S 636
 
5.0%
C 571
 
4.5%
m 538
 
4.3%
P 514
 
4.1%
s 413
 
3.3%
D 394
 
3.1%
Other values (39) 5625
44.5%
Common
ValueCountFrequency (%)
1825
38.1%
0 933
19.5%
- 399
 
8.3%
5 284
 
5.9%
3 275
 
5.7%
1 254
 
5.3%
2 198
 
4.1%
7 184
 
3.8%
4 162
 
3.4%
6 108
 
2.3%
Other values (6) 168
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17420
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1825
 
10.5%
o 1344
 
7.7%
i 1054
 
6.1%
0 933
 
5.4%
n 800
 
4.6%
a 741
 
4.3%
S 636
 
3.7%
C 571
 
3.3%
m 538
 
3.1%
P 514
 
3.0%
Other values (55) 8464
48.6%

Release date
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2003.5906
Minimum1994
Maximum2007
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-09-18T22:12:54.270453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1994
5-th percentile1999
Q12002
median2004
Q32006
95-th percentile2007
Maximum2007
Range13
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.7247548
Coefficient of variation (CV)0.0013599359
Kurtosis-0.45666086
Mean2003.5906
Median Absolute Deviation (MAD)2
Skewness-0.61148389
Sum2079727
Variance7.4242888
MonotonicityNot monotonic
2023-09-18T22:12:54.474510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2007 163
15.7%
2006 153
14.7%
2005 143
13.8%
2004 141
13.6%
2003 101
9.7%
2002 89
8.6%
2001 85
8.2%
2000 61
 
5.9%
1999 53
 
5.1%
1998 32
 
3.1%
Other values (4) 17
 
1.6%
ValueCountFrequency (%)
1994 1
 
0.1%
1995 1
 
0.1%
1996 4
 
0.4%
1997 11
 
1.1%
1998 32
 
3.1%
1999 53
5.1%
2000 61
5.9%
2001 85
8.2%
2002 89
8.6%
2003 101
9.7%
ValueCountFrequency (%)
2007 163
15.7%
2006 153
14.7%
2005 143
13.8%
2004 141
13.6%
2003 101
9.7%
2002 89
8.6%
2001 85
8.2%
2000 61
 
5.9%
1999 53
 
5.1%
1998 32
 
3.1%

Max resolution
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2474.6724
Minimum0
Maximum5616
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-09-18T22:12:54.716283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1280
Q12048
median2560
Q33072
95-th percentile3648
Maximum5616
Range5616
Interquartile range (IQR)1024

Descriptive statistics

Standard deviation759.51361
Coefficient of variation (CV)0.3069148
Kurtosis0.072743219
Mean2474.6724
Median Absolute Deviation (MAD)512
Skewness0.015023355
Sum2568710
Variance576860.92
MonotonicityNot monotonic
2023-09-18T22:12:54.986225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3072 108
 
10.4%
2048 102
 
9.8%
1600 83
 
8.0%
3264 73
 
7.0%
2816 68
 
6.6%
2592 67
 
6.5%
2560 62
 
6.0%
1280 57
 
5.5%
2272 44
 
4.2%
2304 37
 
3.6%
Other values (89) 337
32.5%
ValueCountFrequency (%)
0 1
 
0.1%
512 1
 
0.1%
640 13
 
1.3%
832 1
 
0.1%
1024 11
 
1.1%
1152 12
 
1.2%
1216 1
 
0.1%
1280 57
5.5%
1344 2
 
0.2%
1368 1
 
0.1%
ValueCountFrequency (%)
5616 1
 
0.1%
4992 1
 
0.1%
4536 1
 
0.1%
4500 2
 
0.2%
4368 1
 
0.1%
4288 3
0.3%
4272 1
 
0.1%
4256 5
0.5%
4224 2
 
0.2%
4064 1
 
0.1%

Low resolution
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct70
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1773.9364
Minimum0
Maximum4992
Zeros54
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-09-18T22:12:55.214284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11120
median2048
Q32560
95-th percentile3024
Maximum4992
Range4992
Interquartile range (IQR)1440

Descriptive statistics

Standard deviation830.89795
Coefficient of variation (CV)0.46839219
Kurtosis-0.38898617
Mean1773.9364
Median Absolute Deviation (MAD)528
Skewness-0.29740892
Sum1841346
Variance690391.41
MonotonicityNot monotonic
2023-09-18T22:12:55.533720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2048 187
18.0%
1600 162
15.6%
2592 119
11.5%
640 88
8.5%
1024 70
 
6.7%
2560 60
 
5.8%
0 54
 
5.2%
1280 42
 
4.0%
2304 33
 
3.2%
2272 19
 
1.8%
Other values (60) 204
19.7%
ValueCountFrequency (%)
0 54
5.2%
320 8
 
0.8%
512 4
 
0.4%
576 3
 
0.3%
640 88
8.5%
768 3
 
0.3%
800 17
 
1.6%
876 1
 
0.1%
896 9
 
0.9%
900 2
 
0.2%
ValueCountFrequency (%)
4992 1
 
0.1%
3896 1
 
0.1%
3840 2
 
0.2%
3600 1
 
0.1%
3476 2
 
0.2%
3456 1
 
0.1%
3264 15
1.4%
3216 3
 
0.3%
3200 8
0.8%
3184 1
 
0.1%

Effective pixels
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5963391
Minimum0
Maximum21
Zeros35
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-09-18T22:12:55.801907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q37
95-th percentile10
Maximum21
Range21
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.8440444
Coefficient of variation (CV)0.61876295
Kurtosis0.78656387
Mean4.5963391
Median Absolute Deviation (MAD)2
Skewness0.63289165
Sum4771
Variance8.0885883
MonotonicityNot monotonic
2023-09-18T22:12:55.990107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3 197
19.0%
1 152
14.6%
6 119
11.5%
7 115
11.1%
5 114
11.0%
4 101
9.7%
8 86
8.3%
2 50
 
4.8%
10 39
 
3.8%
0 35
 
3.4%
Other values (6) 30
 
2.9%
ValueCountFrequency (%)
0 35
 
3.4%
1 152
14.6%
2 50
 
4.8%
3 197
19.0%
4 101
9.7%
5 114
11.0%
6 119
11.5%
7 115
11.1%
8 86
8.3%
9 6
 
0.6%
ValueCountFrequency (%)
21 1
 
0.1%
16 1
 
0.1%
13 3
 
0.3%
12 18
 
1.7%
11 1
 
0.1%
10 39
 
3.8%
9 6
 
0.6%
8 86
8.3%
7 115
11.1%
6 119
11.5%

Zoom wide (W)
Real number (ℝ)

ZEROS 

Distinct25
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.963391
Minimum0
Maximum52
Zeros85
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-09-18T22:12:56.188673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q135
median36
Q338
95-th percentile39
Maximum52
Range52
Interquartile range (IQR)3

Descriptive statistics

Standard deviation10.333149
Coefficient of variation (CV)0.31347349
Kurtosis5.6260212
Mean32.963391
Median Absolute Deviation (MAD)2
Skewness-2.5806039
Sum34216
Variance106.77397
MonotonicityNot monotonic
2023-09-18T22:12:56.390060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
38 259
25.0%
35 252
24.3%
36 111
10.7%
0 85
 
8.2%
37 75
 
7.2%
28 61
 
5.9%
39 51
 
4.9%
34 44
 
4.2%
33 24
 
2.3%
32 15
 
1.4%
Other values (15) 61
 
5.9%
ValueCountFrequency (%)
0 85
8.2%
23 1
 
0.1%
24 4
 
0.4%
27 4
 
0.4%
28 61
5.9%
29 3
 
0.3%
30 3
 
0.3%
31 4
 
0.4%
32 15
 
1.4%
33 24
 
2.3%
ValueCountFrequency (%)
52 2
 
0.2%
51 1
 
0.1%
50 2
 
0.2%
47 1
 
0.1%
45 3
 
0.3%
43 5
 
0.5%
42 3
 
0.3%
41 12
 
1.2%
40 13
 
1.3%
39 51
4.9%

Zoom tele (T)
Real number (ℝ)

ZEROS 

Distinct100
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.52505
Minimum0
Maximum518
Zeros85
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-09-18T22:12:56.594513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q196
median108
Q3117
95-th percentile380
Maximum518
Range518
Interquartile range (IQR)21

Descriptive statistics

Standard deviation93.455422
Coefficient of variation (CV)0.76902189
Kurtosis3.9672937
Mean121.52505
Median Absolute Deviation (MAD)9
Skewness1.8776868
Sum126143
Variance8733.916
MonotonicityNot monotonic
2023-09-18T22:12:56.792904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
114 163
 
15.7%
105 139
 
13.4%
0 85
 
8.2%
108 52
 
5.0%
102 43
 
4.1%
111 30
 
2.9%
117 30
 
2.9%
35 25
 
2.4%
140 23
 
2.2%
380 23
 
2.2%
Other values (90) 425
40.9%
ValueCountFrequency (%)
0 85
8.2%
28 2
 
0.2%
31 1
 
0.1%
33 15
 
1.4%
35 25
 
2.4%
36 20
 
1.9%
37 9
 
0.9%
38 16
 
1.5%
39 10
 
1.0%
40 2
 
0.2%
ValueCountFrequency (%)
518 1
 
0.1%
504 2
 
0.2%
486 2
 
0.2%
465 2
 
0.2%
432 12
1.2%
423 1
 
0.1%
420 10
1.0%
400 3
 
0.3%
390 2
 
0.2%
380 23
2.2%

Normal focus range
Real number (ℝ)

ZEROS 

Distinct32
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.145472
Minimum0
Maximum120
Zeros137
Zeros (%)13.2%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-09-18T22:12:56.979704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q130
median50
Q360
95-th percentile80
Maximum120
Range120
Interquartile range (IQR)30

Descriptive statistics

Standard deviation24.141959
Coefficient of variation (CV)0.54687282
Kurtosis-0.41971336
Mean44.145472
Median Absolute Deviation (MAD)10
Skewness-0.41443254
Sum45823
Variance582.83417
MonotonicityNot monotonic
2023-09-18T22:12:57.165670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
50 286
27.6%
60 159
15.3%
0 137
13.2%
80 106
 
10.2%
30 97
 
9.3%
40 89
 
8.6%
70 27
 
2.6%
10 23
 
2.2%
20 14
 
1.3%
45 11
 
1.1%
Other values (22) 89
 
8.6%
ValueCountFrequency (%)
0 137
13.2%
1 6
 
0.6%
4 4
 
0.4%
8 2
 
0.2%
10 23
 
2.2%
12 2
 
0.2%
19 1
 
0.1%
20 14
 
1.3%
23 3
 
0.3%
24 4
 
0.4%
ValueCountFrequency (%)
120 1
 
0.1%
100 2
 
0.2%
90 9
 
0.9%
85 2
 
0.2%
80 106
10.2%
76 6
 
0.6%
75 5
 
0.5%
70 27
 
2.6%
66 1
 
0.1%
60 159
15.3%

Macro focus range
Real number (ℝ)

ZEROS 

Distinct29
Distinct (%)2.8%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean7.7878496
Minimum0
Maximum85
Zeros127
Zeros (%)12.2%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-09-18T22:12:57.350646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median6
Q310
95-th percentile20
Maximum85
Range85
Interquartile range (IQR)7

Descriptive statistics

Standard deviation8.1000814
Coefficient of variation (CV)1.0400922
Kurtosis25.692419
Mean7.7878496
Median Absolute Deviation (MAD)4
Skewness3.6479508
Sum8076
Variance65.611319
MonotonicityNot monotonic
2023-09-18T22:12:57.553222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
10 200
19.3%
5 132
12.7%
0 127
12.2%
1 83
8.0%
20 70
 
6.7%
4 65
 
6.3%
6 61
 
5.9%
3 49
 
4.7%
2 48
 
4.6%
8 34
 
3.3%
Other values (19) 168
16.2%
ValueCountFrequency (%)
0 127
12.2%
1 83
8.0%
2 48
 
4.6%
3 49
 
4.7%
4 65
6.3%
5 132
12.7%
6 61
5.9%
7 26
 
2.5%
8 34
 
3.3%
9 16
 
1.5%
ValueCountFrequency (%)
85 2
 
0.2%
80 1
 
0.1%
60 2
 
0.2%
50 2
 
0.2%
40 2
 
0.2%
30 8
 
0.8%
28 1
 
0.1%
25 10
 
1.0%
22 2
 
0.2%
20 70
6.7%

Storage included
Real number (ℝ)

ZEROS 

Distinct44
Distinct (%)4.2%
Missing2
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean17.447876
Minimum0
Maximum450
Zeros123
Zeros (%)11.8%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-09-18T22:12:57.788452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median16
Q320
95-th percentile32
Maximum450
Range450
Interquartile range (IQR)12

Descriptive statistics

Standard deviation27.440655
Coefficient of variation (CV)1.5727218
Kurtosis148.06061
Mean17.447876
Median Absolute Deviation (MAD)8
Skewness10.719963
Sum18076
Variance752.98955
MonotonicityNot monotonic
2023-09-18T22:12:58.134110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
16 279
26.9%
8 152
14.6%
32 143
13.8%
0 123
11.8%
4 37
 
3.6%
10 25
 
2.4%
14 21
 
2.0%
1 21
 
2.0%
20 18
 
1.7%
11 18
 
1.7%
Other values (34) 199
19.2%
ValueCountFrequency (%)
0 123
11.8%
1 21
 
2.0%
2 10
 
1.0%
4 37
 
3.6%
6 8
 
0.8%
7 3
 
0.3%
8 152
14.6%
9 17
 
1.6%
10 25
 
2.4%
11 18
 
1.7%
ValueCountFrequency (%)
450 2
 
0.2%
340 1
 
0.1%
256 2
 
0.2%
173 1
 
0.1%
128 1
 
0.1%
64 7
0.7%
58 4
0.4%
56 3
0.3%
54 3
0.3%
52 3
0.3%

Weight (inc. batteries)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct237
Distinct (%)22.9%
Missing2
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean319.26544
Minimum0
Maximum1860
Zeros21
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-09-18T22:12:58.317988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile133.5
Q1180
median226
Q3350
95-th percentile829
Maximum1860
Range1860
Interquartile range (IQR)170

Descriptive statistics

Standard deviation260.41014
Coefficient of variation (CV)0.815654
Kurtosis9.8231939
Mean319.26544
Median Absolute Deviation (MAD)62.5
Skewness2.8267815
Sum330759
Variance67813.44
MonotonicityNot monotonic
2023-09-18T22:12:58.557234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
320 34
 
3.3%
180 33
 
3.2%
190 32
 
3.1%
250 29
 
2.8%
170 27
 
2.6%
165 27
 
2.6%
200 25
 
2.4%
195 24
 
2.3%
185 22
 
2.1%
220 22
 
2.1%
Other values (227) 761
73.3%
ValueCountFrequency (%)
0 21
2.0%
100 1
 
0.1%
108 1
 
0.1%
110 1
 
0.1%
115 3
 
0.3%
116 1
 
0.1%
118 2
 
0.2%
120 9
0.9%
122 1
 
0.1%
123 1
 
0.1%
ValueCountFrequency (%)
1860 1
 
0.1%
1800 1
 
0.1%
1700 2
0.2%
1650 2
0.2%
1585 2
0.2%
1580 4
0.4%
1565 3
0.3%
1385 1
 
0.1%
1335 1
 
0.1%
1300 1
 
0.1%

Dimensions
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)9.7%
Missing2
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean105.36342
Minimum0
Maximum240
Zeros14
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-09-18T22:12:58.760181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile83
Q192
median101
Q3115
95-th percentile147
Maximum240
Range240
Interquartile range (IQR)23

Descriptive statistics

Standard deviation24.262761
Coefficient of variation (CV)0.2302769
Kurtosis5.377189
Mean105.36342
Median Absolute Deviation (MAD)10
Skewness-0.30674357
Sum109156.5
Variance588.68157
MonotonicityNot monotonic
2023-09-18T22:12:58.979348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 48
 
4.6%
91 47
 
4.5%
95 41
 
3.9%
100 38
 
3.7%
106 31
 
3.0%
108 30
 
2.9%
92 30
 
2.9%
94 30
 
2.9%
110 26
 
2.5%
97 25
 
2.4%
Other values (91) 690
66.5%
ValueCountFrequency (%)
0 14
1.3%
30 1
 
0.1%
32 2
 
0.2%
38 1
 
0.1%
51 2
 
0.2%
54 1
 
0.1%
60 2
 
0.2%
65 3
 
0.3%
67 2
 
0.2%
72 2
 
0.2%
ValueCountFrequency (%)
240 1
 
0.1%
194 5
0.5%
170 2
 
0.2%
162 3
0.3%
161 2
 
0.2%
160 2
 
0.2%
158 5
0.5%
157 6
0.6%
156 6
0.6%
153 2
 
0.2%

Price
Real number (ℝ)

Distinct43
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean457.38439
Minimum14
Maximum7999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-09-18T22:12:59.184243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile126
Q1149
median199
Q3399
95-th percentile1699
Maximum7999
Range7985
Interquartile range (IQR)250

Descriptive statistics

Standard deviation760.45292
Coefficient of variation (CV)1.6626123
Kurtosis37.084446
Mean457.38439
Median Absolute Deviation (MAD)70
Skewness5.1869571
Sum474765
Variance578288.64
MonotonicityNot monotonic
2023-09-18T22:12:59.416970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
149 112
 
10.8%
229 103
 
9.9%
129 92
 
8.9%
169 67
 
6.5%
179 63
 
6.1%
249 52
 
5.0%
199 50
 
4.8%
1699 47
 
4.5%
399 46
 
4.4%
126 41
 
3.9%
Other values (33) 365
35.2%
ValueCountFrequency (%)
14 2
 
0.2%
19 2
 
0.2%
62 10
 
1.0%
99 32
 
3.1%
119 4
 
0.4%
126 41
 
3.9%
129 92
8.9%
139 34
 
3.3%
146 8
 
0.8%
149 112
10.8%
ValueCountFrequency (%)
7999 3
 
0.3%
4999 3
 
0.3%
4699 4
 
0.4%
4499 5
 
0.5%
2499 1
 
0.1%
1799 1
 
0.1%
1699 47
4.5%
1599 10
 
1.0%
1499 30
2.9%
1399 24
2.3%

Interactions

2023-09-18T22:12:49.715304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:28.977893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:30.848378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:32.534363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:34.254585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:35.895769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:37.606327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:39.385670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:41.150582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:42.976819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:44.906032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:47.023659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:50.013778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:29.167680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:31.011935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:32.682288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:34.412536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:36.031333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:37.747317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:39.527710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:41.292279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:43.148377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:45.064852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:47.193820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:50.243377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:29.312186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:31.149968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:32.823453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:34.553868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:36.160979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:37.880831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:39.681594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:41.421196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:43.312858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:45.210463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:47.354569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:50.393504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:29.447300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:31.308155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:32.954534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:34.685067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:36.295038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:38.009120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:39.834344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:41.606137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:43.464176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:45.349958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:47.515632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:50.523147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:29.664340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:31.440053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:33.083773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:34.813005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:36.433045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:38.135745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:39.970553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:41.766137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:43.656476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:45.502905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:47.732647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:50.666977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:29.796252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:31.570292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:33.214228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:34.949896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:36.576731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:38.291725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:40.136425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:41.899631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:43.810068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:45.680445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:47.963903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:50.833074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:29.925149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:31.707105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:33.362868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:35.077654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:36.712784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:38.423173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:40.277153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:42.041251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:43.955461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:45.837696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:48.289692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:50.998738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:30.055880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:31.840242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:33.508198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:35.217821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:36.852962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:38.705052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:40.435717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:42.183517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:44.100973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:46.055899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:48.734801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:51.144134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:30.222321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:31.967391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:33.654352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:35.343622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:37.005927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:38.826581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:40.564255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:42.323488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:44.230753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:46.284399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:48.918766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:51.327579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:30.368759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:32.091018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:33.796025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:35.481131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:37.147240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:38.964263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:40.712231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:42.475549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:44.370373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:46.464482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:49.111319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:51.484505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:30.525251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:32.243062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:33.949872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:35.626802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:37.305129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:39.107637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:40.860313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:42.634314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:44.521060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:46.646813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:49.316049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:51.630475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:30.676769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:32.393198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:34.108318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:35.753530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:37.437322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:39.240694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:41.003772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:42.804074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:44.716028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:46.797576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-18T22:12:49.504999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-09-18T22:12:59.577401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Release dateMax resolutionLow resolutionEffective pixelsZoom wide (W)Zoom tele (T)Normal focus rangeMacro focus rangeStorage includedWeight (inc. batteries)DimensionsPrice
Release date1.0000.8270.7770.859-0.0670.268-0.092-0.2450.385-0.409-0.340-0.011
Max resolution0.8271.0000.8780.975-0.2300.186-0.162-0.3450.324-0.144-0.1410.101
Low resolution0.7770.8781.0000.869-0.1630.233-0.133-0.3140.307-0.170-0.1770.151
Effective pixels0.8590.9750.8691.000-0.2100.203-0.163-0.3330.333-0.186-0.1560.085
Zoom wide (W)-0.067-0.230-0.163-0.2101.0000.4480.2560.2350.161-0.259-0.239-0.156
Zoom tele (T)0.2680.1860.2330.2030.4481.0000.1670.0020.381-0.061-0.185-0.147
Normal focus range-0.092-0.162-0.133-0.1630.2560.1671.0000.4800.200-0.145-0.136-0.198
Macro focus range-0.245-0.345-0.314-0.3330.2350.0020.4801.0000.064-0.177-0.185-0.135
Storage included0.3850.3240.3070.3330.1610.3810.2000.0641.000-0.317-0.349-0.175
Weight (inc. batteries)-0.409-0.144-0.170-0.186-0.259-0.061-0.145-0.177-0.3171.0000.7330.141
Dimensions-0.340-0.141-0.177-0.156-0.239-0.185-0.136-0.185-0.3490.7331.0000.152
Price-0.0110.1010.1510.085-0.156-0.147-0.198-0.135-0.1750.1410.1521.000

Missing values

2023-09-18T22:12:51.854044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-18T22:12:52.159383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-09-18T22:12:52.442966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ModelRelease dateMax resolutionLow resolutionEffective pixelsZoom wide (W)Zoom tele (T)Normal focus rangeMacro focus rangeStorage includedWeight (inc. batteries)DimensionsPrice
0Agfa ePhoto 128019971024.0640.00.038.0114.070.040.04.0420.095.0179.0
1Agfa ePhoto 168019981280.0640.01.038.0114.050.00.04.0420.0158.0179.0
2Agfa ePhoto CL182000640.00.00.045.045.00.00.02.00.00.0179.0
3Agfa ePhoto CL3019991152.0640.00.035.035.00.00.04.00.00.0269.0
4Agfa ePhoto CL30 Clik!19991152.0640.00.043.043.050.00.040.0300.0128.01299.0
5Agfa ePhoto CL4520011600.0640.01.051.051.050.020.08.0270.0119.0179.0
6Agfa ePhoto CL5019991280.0640.01.034.0102.00.00.08.00.00.0179.0
7Canon PowerShot 3501997640.00.00.042.042.070.03.02.0320.093.0149.0
8Canon PowerShot 6001996832.0640.00.050.050.040.010.01.0460.0160.0139.0
9Canon PowerShot A1020011280.01024.01.035.0105.076.016.08.0375.0110.0139.0
ModelRelease dateMax resolutionLow resolutionEffective pixelsZoom wide (W)Zoom tele (T)Normal focus rangeMacro focus rangeStorage includedWeight (inc. batteries)DimensionsPrice
1028Toshiba PDR-M319991280.0640.01.038.0115.090.025.04.0380.0122.062.0
1029Toshiba PDR-M419991600.0800.01.040.040.050.010.08.0290.0112.062.0
1030Toshiba PDR-M519991600.0800.01.040.0120.090.025.08.0350.0130.062.0
1031Toshiba PDR-M6020001792.0896.02.038.086.050.05.04.0320.0121.0449.0
1032Toshiba PDR-M6120011792.0896.02.038.0114.050.05.08.0310.0123.062.0
1033Toshiba PDR-M6520012048.01024.03.038.0114.010.010.08.0320.0120.062.0
1034Toshiba PDR-M7020002048.01024.03.035.0105.080.09.016.0390.0116.062.0
1035Toshiba PDR-M7120012048.01024.03.035.098.080.010.08.0340.0107.062.0
1036Toshiba PDR-M8120012400.01200.03.035.098.080.010.016.0340.0107.062.0
1037Toshiba PDR-T1020021600.0800.01.038.038.040.020.08.0180.086.0129.0